SYSTEM AND METHOD FOR AN OPTIMIZED, SELF-LEARNING AND SELF-ORGANIZING CONTACT CENTER

• US 20170169325A1
• Filed: 02/28/2017
• Published: 06/15/2017
• Est. Priority Date: 05/23/2014
• Status: Active Grant

• Current Assignee: Vonage Business Limited (Telefonaktiebolaget LM Ericsson)
• Original Assignee: NewVoiceMedia Limited (Telefonaktiebolaget LM Ericsson)
• Inventors: McCord, Alan, Unitt, Ashley
• Granted Patent: US 10,586,175 B2
• Time in Patent Office: Days
• CPC Class Codes: G06F 16/3329 Natural language query formulation or dialogue systems
• CPC Class Codes: G06F 16/35 Clustering; Classification
• CPC Class Codes: G06F 40/205 Parsing
• CPC Class Codes: G06F 40/30 Semantic analysis
• CPC Class Codes: G06N 20/00 Machine learning
• CPC Class Codes: G06N 7/01 Probabilistic graphical models, e.g. probabilistic networks
• CPC Class Codes: H04M 3/5183 Call or contact centers with computer-telephony arrangements
• CPC Class Codes: H04M 3/5233 Operator skill based call distribution
• CPC Class Codes: H04M 3/5235 Dependent on call type or called number [DNIS]

Abstract

A system and method for an optimized, self-learning and self-organizing contact center has been developed. This system and method uses principles and tools of information theory, including the latent Dirichlet allocation which reduces information to specific predetermined topics and a distribution of topic related words to infer its hidden, generative underpinnings so to self-organize a contact center, infer its desired electronic versus human make up, and optimally route all customer requests to an electronic resource or a specific human agent best suited to respond to the request for maximal business value per interaction.

Claims

1. A system for an optimized, self-learning and self-organizing contact center comprising:

a topic-based routing module stored in a memory of and operating on a processor of a computing device;

an interaction information optimization module stored in a memory of and operating on a processor of a computing device; and

wherein the interaction information optimization module;

monitors all communication into and out of the contact center;

analyzes each communication stream to identify topic-related identifiers; and

creates optimized relationships between topic-related identifiers present in analyzed communications and business value-related topics attached to contact center resources using information theory algorithms and machine learning; and

wherein the topic based routing module;

receives a text request for assistance from a user via a network;

infers the topic distribution of the request based using an information theory algorithm;

receives an optimal contact center resource to service the request for information from the interaction information optimization module; and

routes the request to the contact center resource best suited to respond to the request.

2. The system of claim 1, wherein the interaction information is optimized using information distance calculations.

3. The system of claim 1, wherein an information theory algorithm used may be based upon the latent Dirichlet allocation to infer hidden generative data in support of interactive information optimization.

4. The system of claim 1, wherein the topic related identifiers may be categorical or numerical.